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Table 4 Comparison of clustering methods on two data sets

From: A clustering-based approach for efficient identification of microRNA combinatorial biomarkers

Feature combination

Performance measure

GSE22220

GSE40525

  

HCb

RCa

HCb

RCa

Pair

AvgRank 10

7

8.2

8.5

9.1

 

AvgRank 100

92.2

84.4

180.9

103.0

 

AvgRank 1000

2003.1

1624.4

10696.8

1470.5

 

HitRatio 10(%)

70.0

70.0

60.0

40.0

 

HitRatio 100(%)

54.0

58.0

32.0

64.0

 

HitRatio 1000(%)

30.0

32.0

15.0

39.2

Triple

AvgRank 10

8.2

8.6

9.2

26.3

 

AvgRank 100

95.4

94.3

68.7

229.4

 

AvgRank 1000

1776.2

1684.3

3675.2

1577.2

 

HitRatio 10(%)

60.0

60.0

20.0

20.0

 

HitRatio 100(%)

58.0

58.0

71.0

66.0

 

HitRatio 1000(%)

27.1

27.1

30.3

36.8

Quadruple

AvgRank 10

14.2

12.8

9.0

174.0

 

AvgRank 100

112.6

108.5

257.2

273.0

 

AvgRank 1000

1639.1

1482.3

3171.3

2826.6

 

HitRatio 10(%)

30.0

40.0

78.0

40.0

 

HitRatio 100(%)

48.0

50.0

12.0

4.0

 

HitRatio 1000(%)

23.2

26.4

16.0

19.3

  1. aRC: refined clustering, in which the inconsistency coefficient for raw clusters and thresholds of MSL and MLR are fixed
  2. bHC: hierarchical clustering, which performs the best by trying different inconsistency coefficients